Patient-reported health-related quality of life as complex patient outcomes in stroke survivors

项目摘要 在美国，每年约有70万美国人中风，导致相当高的发病率 和死亡率。中风影响健康相关生活质量的多个领域，在老龄化的美国， 我们可能会看到中风的增加，因为中风的风险随着年龄的增加而增加。标准衡量标准 对卒中后患者预后的评估是改良的Rankin量表(MRS)，该量表主要关注患者的机动性 并且对HRQL的其他领域(例如，认知功能、抑郁、疲劳、焦虑)的区分性较小。 然而，患者往往有复杂的患者结局，并有不同程度的HRQOL异常 多个域，关于这些HRQOL分数是如何聚集在一起的知之甚少。减少 除移动性以外的HRQOL仍然难以预测，限制了患者结果的评估和管理。 为了满足这一需求，我们将确定包含多个HRQOL域和 根据指数期间收集的变量预测3个月随访的个体复杂患者结果 住院治疗。识别中风后复杂的患者结局可能有助于临床医生和研究人员迅速 理解结果的多维模式，并更好地告知患者和家属。这项研究的 目的是评估不同的HRQOL领域作为复杂患者结局的驱动因素 以预测中风幸存者的复杂患者结局。如果中风后的随访护理能更有效率 可以根据中风患者的指数来预测和主动解决他们的复杂需求 住院治疗。为了达到目标，目标1将确定没有很好地描述的HRQOL域 患者预后的标准MRS。目标2将确定跨多个领域的复杂患者结果 3种主要卒中类型(即AIS、ICH、SAH)患者的HRQOL。AIM 3将预测复杂的患者 根据指数住院期间积累的患者数据进行随访的结果。我们将使用来自3的数据 资料来源：1)西北大学脑攻击登记处(Nubar)，一个预期收集的 电子健康记录，包含中风后患者预后的详细信息，2)西北 医疗企业数据仓库(NMEDW)，一个涵盖11家医院的强大数据基础设施 收集和存储600多万独立患者的电子健康记录，以及3) 急性脑出血-II(ATACH-II)的降压治疗--1,000例临床试验数据集 脑出血患者。这项研究的发现将：1)确定当前全球 结果测量，2)为定义中风后复杂的患者结果贡献信息 对于多个HRQOL领域，以及3)建议个体复杂的患者在3个月的随访结果- 可以在患者首次住院期间预测UP。这项研究与AHRQ的使命和战略一致 通过解决两个优先领域：1)患者报告的结果，以及2)利用数据和技术提供 以人为中心协调健康和行为健康的患者360度视角。

Project Summary In the U.S., approximately 700,000 Americans experience a stroke each year, resulting in substantial morbidity and mortality. Stroke affects multiple domains of health-related quality of life (HRQoL), and in an aging U.S., we are likely to see an increase in stroke because the risk of stroke increases with age. The standard measure for patient outcomes after stroke is the modified Rankin Scale (mRS) which heavily focuses on patient mobility and is less discriminating for other domains of HRQoL (e.g., cognitive function, depression, fatigue, anxiety). However, patients often have complex patient outcomes with varying degrees of abnormal HRQoL across multiple domains, and not much is known about how these HRQoL scores cluster together. Reductions in HRQoL other than mobility remain difficult to predict, limiting patient outcomes assessment and management. To address this need, we will identify complex patient outcomes that incorporate multiple HRQoL domains and predict individual complex patient outcomes at 3-month follow-up from variables collected during the index hospitalization. Identifying complex patient outcomes after stroke may help clinicians and researchers quickly comprehend the multi-dimensional patterns of outcomes and better inform patients and families. The study's objectives are to assess differentially identified HRQoL domains as the driver of complex patient outcomes and to predict complex patient outcomes for stroke survivors. Follow-up care after stroke could be more efficient if it were possible to anticipate and proactively address the complex needs of stroke patients based on their index hospital stay. To meet the objectives, Aim 1 will determine the HRQoL domains that are not well described by the standard mRS for patient outcomes. Aim 2 will identify complex patient outcomes across multiple domains of HRQoL in patients with the 3 major types of stroke (i.e., AIS, ICH, SAH). Aim 3 will predict complex patient outcomes at follow-up from patient data accumulated during their index hospitalization. We will use data from 3 sources: 1) the Northwestern University Brain Attack Registry (NUBAR), a prospectively collected registry of electronic health records with detailed information on patient outcomes after stroke, 2) the Northwestern Medicine Enterprise Data Warehouse (NMEDW), a robust data infrastructure spanning 11-hospitals that collects and stores electronic health records for more than 6 million unique patients, and 3) the Antihypertensive Treatment of Acute Cerebral Hemorrhage-II (ATACH-II), a clinical trial dataset of 1,000 patients with ICH. Findings from this study will: 1) identify the HRQoL domains obscured by current global outcome measures, 2) contribute information for defining complex patient outcomes after stroke that account for multiple HRQoL domains, and 3) suggest whether individual complex patient outcomes at 3-month follow- up can be predicted during a patient's initial hospitalization. The study aligns with AHRQ's mission and strategy by addressing 2 priority areas: 1) patient-reported outcomes, and 2) harnessing data and technology to provide a 360-degree view of the patient that coordinates health and behavioral health in a person-centered manner.